Modeling Individual Vulnerability to Communicable Diseases: A Framework and Design

Bian, Ling; Huang, Yuxia; Mao, Liang; Lim, Eunjung; Lee, Gyoungju; Yang, Yan; Cohen, Murray; Wilson, Douglas

doi:10.1080/00045608.2012.674844

Cited by 36 publications

(36 citation statements)

References 21 publications

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“…We use the framework designed by Bian et al to run many simulations [3]. In order to attempt to produce reproducible results, each type of simulation is run 500 times.…”

Section: Simulations and Resultsmentioning

confidence: 99%

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Realistic Modeling and Simulation of Influenza Transmission Over an Urban Community

2015

SIURO

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Infectious diseases that are spread through human contact can progress very rapidly in a population. One of the key factors in the spreading of contagion, and a main concern in attempting to stop the spread of illness, is the particular configuration of links among individuals in local communities within the larger population. This study uses a detailed individual-based, three-partite model comprising about 245,000 individuals located in an urban area in the Northeastern United States. Interactions among individuals are divided into family, workplace and pastime (service places, shopping, etc.), each occurring during a separate time period (daytime, pastime, and nighttime). Thus, the network allows one to model the spatial and temporal heterogeneity in the transmission of communicable diseases and to capture the differences between various individuals' vulnerability to infection. We performed Monte-Carlo simulations of the spreading of influenza through this network. Simulation results correspond well to the reported epidemic information. Results also demonstrate a temporal and population threshold which if exceeded, result in the long-term spread of infection. We expect that the findings will offer a valuable platform to devise spatially and temporally oriented control and intervention strategies for communicable diseases.

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“…We use the framework designed by Bian et al to run many simulations [3]. In order to attempt to produce reproducible results, each type of simulation is run 500 times.…”

Section: Simulations and Resultsmentioning

confidence: 99%

“…A person cycles through multiple different networks and therefore has different vulnerabilities to infection based on contact and on his or her own age group. This is all accounted for in this individual-based modeling system, which is an extension and further exploration of the data-verified framework designed by Bian et al [3].…”

Section: Networkmentioning

confidence: 99%

Realistic Modeling and Simulation of Influenza Transmission Over an Urban Community

2015

SIURO

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“…Dykes and Mountain (2003) discuss data collection techniques by mobile phone, GPS and location-based services and suggest a visual analytical method to deal with the data gathered (Dykes and Mountain 2003). Lam (2012) and Bian et al (2012) both discuss ongoing challenges to health risk assessment despite the wider availability of individual level data (Bian et al 2012; Lam 2012). …”

Section: Space-time Geovisualization and Modelingmentioning

confidence: 99%

Genetic GIScience: Toward a Place-Based Synthesis of the Genome, Exposome, and Behavome

Jacquez¹,

Sabel²,

Chen³

2015

Annals of the Association of American Geographers

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The exposome, defined as the totality of an individual’s exposures over the life course, is a seminal concept in the environmental health sciences. Although inherently geographic, the exposome as yet is unfamiliar to many geographers. This article proposes a place-based synthesis, genetic geographic information science (Genetic GISc) that is founded on the exposome, genome+ and behavome. It provides an improved understanding of human health in relation to biology (the genome+), environmental exposures (the exposome), and their social, societal and behavioral determinants (the behavome). Genetic GISc poses three key needs: First, a mathematical foundation for emergent theory; Second, process-based models that bridge biological and geographic scales; Third, biologically plausible estimates of space-time disease lags. Compartmental models are a possible solution; this article develops two models using pancreatic cancer as an exemplar. The first models carcinogenesis based on the cascade of mutations and cellular changes that lead to metastatic cancer. The second models cancer stages by diagnostic criteria. These provide empirical estimates of the distribution of latencies in cellular states and disease stages, and maps of the burden of yet to be diagnosed disease. This approach links our emerging knowledge of genomics to cancer progression at the cellular level, to individuals and their cancer stage at diagnosis, to geographic distributions of cancer in extant populations. These methodological developments and exemplar provide the basis for a new synthesis in health geography: genetic geographic information science.

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“…This study uses a contact network previously constructed for a metropolitan community in the Northeastern US (Bian et al 2012). Each individual belongs to a family and most belong to a workplace.…”

Section: Contact Networkmentioning

confidence: 99%

Exploring the Structure of Contact Networks at Multiple Scales

Gao¹,

Bian²

2016

GIScience

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This study examines the scale effects on the network structure and spatial structure of contact networks. Regular grids with different cell sizes are used to divide one observed and two reference random networks into multiple scales. Four metrics are used to represent the two structures. Results show that the network structure of the observed network is sensitive to scale changes at fine scales. In comparison, the clustered spatial structure is scale independent.

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Modeling Individual Vulnerability to Communicable Diseases: A Framework and Design

Cited by 36 publications

References 21 publications

Realistic Modeling and Simulation of Influenza Transmission Over an Urban Community

Realistic Modeling and Simulation of Influenza Transmission Over an Urban Community

Genetic GIScience: Toward a Place-Based Synthesis of the Genome, Exposome, and Behavome

Exploring the Structure of Contact Networks at Multiple Scales

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